Located at the top of the red giants in Hertzsprung-Russell diagram, M giants are the brightest stars that evolved from the sun-like main sequence stars. The study of M giants is crucial to understand the Milky Way, especially the Galactic haloes. The spectrum of an M giants in medium and low resolution is often mixed with spectra of M dwarfs because of insignificant features, noise effects, and other factors. Previous studies often used the molecular index of CaH2+CaH3 vs. TiO5 to search for M giant candidates, then checked them with human eyes. However, this method only used three important molecular band indices associated with giants, without using other spectral features to identify the M giants, which may cause misclassification due to noise pollution of the index. Moreover, relying on human eyes to check a large number of spectra is time-consuming, and the quality of the inspection dependings on people’s experience and its reliability is not guaranteed. Since 2011, LAMOST has released more than 9 million celestial spectra. The latest spectral data product data release 5(DR5) contains 520 000 M-type spectral data, which needs an automatic, accurate and effective method to distinguish the M sub-samples of different luminosity levels. This study uses four ensemble tree models: Random Forest, GBDT, XGBoost, and LightGBM to construct classifiers that distinguish between M giants and M dwarfs. The accuracy of four classifiers is 97.23%, 98%, 98.05%, and 98.32%, respectively. Experiments showed that LightGBM has higher accuracy and less training time when compared to the other threemodels. The analysis of important features obtained by the classifier models showed that ensemble tree model can efficiently extract and express the structural features that distinguish M giants and M dwarfs. These features include not only the atomic lines, molecular bands, but also their adjacent pseudo-continuum spectrum, which is consistent with the features and pseudo-continuum spectra that we traditionally need to calculate the indices. Compared to the traditional classification methods, ensemble tree can use the combination of tens or hundreds important features in the spectrum rather than only several features to avoid misclassification affected by noises. The results of this study showed that the ensemble tree algorithm has significant advantages in the process of M giant recognition, and it can completely replace the traditional M giant spectral discrimination method using only CaH and TiO indices. In this study an effective method has been provided for LAMOST to efficiently and effectively process the massive celestial spectra. As the LAMOST survey continues, more and more M spectra will be accumulated, which provides massive data for the studies of structure and evolution of the Milky Way.